I am measuring protein in humans using two different types of measurement techniques, X and Y (measured on different scales). I have two replications for type X and four for type Y. I average the two measurements for X and average the four measurements for Y to get the 'true' protein average. There are about 120 subjects.
There is large variation in the replication measurements and, surprisingly, the relationship between the average X and the average Y values is not very strong (r = .36).
I am trying to determine which measurement technique is better. I would like to use the subject's level of protein as a predictor in a regression model. I have thought of looking at the coefficient of variations and Cronbach's alphas. Should these two results be consistent? Do they apply here? What would you recommend?
Thanks everyone for the thoughtful responses. I've found out more about the data (it was kind of just thrown at me). The protein measurements were done on consecutive days (so X had two days and Y had 4 days). So naturally someones protein varies from day to day which explains the variability in the measurements. The average is then used as an estimate of their current protein intake. Right now this a common approach for measuring a subjects protein level, since there is no way to know what someones current level of protein intake truly is.
I don't like choosing the protein measurement that correlates strongest with the outcome. I don't think the reviewers will like this justification. Looking at various reliability measurements seems like a better approach to me (I prefer the coefficient of variation). I always thought cronbach's alpha is used for reliability of a latent construct (you could make an argument here I suppose that protein is latent).
Also the correlation is indeed low between X and Y. However I computed that correlation ignoring the the measurement error evident in both X and Y. Correcting for this will lead to a stronger correlation. I have also learned that data collection was sloppy, so not having a strong correlation is not surprising.